Overpeck+ (2011) doi:[10.1126/science.1197869](http://doi.org/10.1126/science.1197869)
Worm+ (2006) doi:[10.1126/science.1132294](http://doi.org/10.1126/science.1132294)
Barnoksy+ (2012) doi:[10.1038/nature11018](http://doi.org/10.1038/nature11018)
credit: NOAA
credit: NASA
credit: NSF
credit: Hopkin (2006) doi:[10.1038/444420a](http://doi.org/10.1038/444420a)
credit: Witze (2013) doi:[10.1038/501480a](http://doi.org/10.1038/501480a)
credit: NERSC
credit: Scambos & Bauer, NSIDC
Overpeck+ (2011) doi:[10.1126/science.1197869](http://doi.org/10.1126/science.1197869)
Baraniuk (2011) doi:[10.1126/science.1197448](http://doi.org/10.1126/science.1197448)
adapted from Reichman+ (2011) doi:[10.1126/science.1197962](http://doi.org/10.1126/science.1197962)
today the visualization and analysis component has become a bottleneck
Fox & Hendler (2011) doi:[10.1126/science.1197654](http://doi.org/10.1126/science.1197654)
Fox & Hendler (2011) doi:[10.1126/science.1197654](http://doi.org/10.1126/science.1197654)
Most scientific data is created in a form that facilitates its generation rather than focusing on its eventual use.
Fox & Hendler (2011) doi:[10.1126/science.1197654](http://doi.org/10.1126/science.1197654)
credit: Arthus-Bertrand
credit: Arthus-Bertrand
building tools, building community
IPCC records and model projections at your fingertips
library("rWBclimate)
country.list <- c("USA", "CAN)
country.dat <- get_historical_temp(country.list, "year)
ggplot(country.dat, aes(x = year, y = data, group = locator)) +
geom_point() + geom_path() + xlab("Year) +
ylab("Average annual temperature) +
stat_smooth(se = F, colour = "black) +
facet_wrap(~locator, scale = "free) + theme_bw()IPCC records and model projections at your fingertips
library("rfisheries)
species <- of_species_codes()
who <- c("TUX", "COD", "VET", "NPA)
by_species <- lapply(who, function(x) of_landings(species = x))
names(by_species) <- who
dat <- melt(by_species, id = c("catch", "year))[, -5]
names(dat) <- c("catch", "year", "species", "a3_code)
ggplot(dat, aes(year, catch)) + geom_line() +
facet_wrap(~a3_code, scales = "free_y) + theme_bw()library("rgbif)Jones+ (2006) doi:[10.1146/annurev.ecolsys.37.091305.110031](http://doi.org/10.1146/annurev.ecolsys.37.091305.110031)
effective interdisciplinary communication of data limitations with regard to, for example,
Overpeck+ (2011) doi:[10.1126/science.1197869](http://doi.org/10.1126/science.1197869)
Evans+ doi:[10.1126/science.1201765](http://doi.org/10.1126/science.1201765)
Mitchner (2012) doi:[10.1016/j.tree.2011.11.016](http://doi.org/10.1016/j.tree.2011.11.016)
Although research scientists have been the main users of these data, an increasing number of resource managers (working in fields such as water, public lands, health, and marine resources) need and are seeking access to climate data to inform their decisions, just as a growing range of policy-makers rely on climate data to develop climate change strategies
- Overpeck+ (2011) doi:[10.1126/science.1197869](http://doi.org/10.1126/science.1197869)
credit: The Economist
Synthesizes over 140 data sets.
How easy would it be to update this to reflect new data?
adapted from Reichman+ (2011) doi:[10.1126/science.1197962](http://doi.org/10.1126/science.1197962)
Reichman+ (2011) doi:[10.1126/science.1197962](http://doi.org/10.1126/science.1197962)
Peng (2011) doi:[10.1126/science.1213847](http://doi.org/10.1126/science.1213847)
Mascarelli (2014) doi:[10.1038/nj7493-523a](http://doi.org/10.1038/nj7493-523a)
credit: The Economist
credit: The Economist
credit: Wikipedia
Global change problems are increasingly data driven, bringing new challenges and opportunities: